In this work, we address the problem of spatio-temporal action detection in temporally untrimmed videos. It is an important and challenging task as finding accurate human actions in both temporal and spatial space is important for analyzing large-scale video data. To tackle this problem, we propose a cascade proposal and location anticipation (CPLA) model for frame-level action detection. There are several salient points of our model: (1) a cascade region proposal network (casRPN) is adopted for action proposal generation and shows better localization accuracy compared with single region proposal network (RPN); (2) action spatio-temporal consistencies are exploited via a location anticipation network (LAN) and thus frame-level action detection is not conducted independently. Frame-level detections are then linked by solving an linking score maximization problem, and temporally trimmed into spatio-temporal action tubes. We demonstrate the effectiveness of our model on the challenging UCF101 and LIRIS-HARL datasets, both achieving state-of-the-art performance.

本研究针对视频中时间未被截断的情况下的时空动作检测问题，提出了基于级联提议和位置预测(CPLA)模型的帧级动作检测方法，其中采用了级联区域提议网络(casRPN)来生成动作提议，利用位置预测网络(LAN)对动作的时空一致性进行了处理，并在UCF101和LIRIS-HARL数据集上展示了模型的有效性。

级联建议和位置预测的时空动作检测